Given the known difficulties with explaining black-boxes [1]}, the existence of unwanted social bias in data and ML models [2]}, the security vulnerabilities of ML (e.g., [3]}, [4]}), and the potentially surprising behavior of black-boxes (e.g., [5]}, [6]}), it appears prudent today to use highly transparent ML mechanisms for applications that make life-altering or high-value decisions. Interpretability, as enabled by interpretable models and post-hoc explanations (see Corollary REF ), may be mandated by regulation for some life- or mission-critical applications, but interpretability is also recommended for any ML application in which inevitable wrong decisions should be appealable. This subsection discusses a few details and examples regarding regulated ML applications and appeal, and also advocates for trust-enhancing DI testing (see Corollaries REF  – REF ) in high-stakes or human-centered applications.
